To tackle these problems, some incremental solutions are introduced. Lu et al. [1]} firstly point out the error propagation problem in learned video compression. They design a joint training strategy to train the video codec by using the information from different time steps in one video clip and combines all the information to optimize the learned codec for better video compression performance. This method can alleviate but not avoid the error propagation problem. In order to reduce the failure cases (e.g., the disocclusion and fast motion cases) derived from the optical flow and bilinear warping operation, a scale-space flow with scale-space warping operation is introduced in [2]}, which achieves the SOTA performance among other learned methods. Implicit optical flow based methods [3]}, [2]} use an encoder network to directly aggregate motion information instead of a pre-trained flow network, reducing the cost of model storage, computation, and training.
